Supervised Machine Learning for Early Cardiovascular Risk Detection in Cloud Computing Environments

Cardio vascular diseases remain one of the major reasons for global morbidity and mortality, thus requiring immediate risk identification and preemptive healthcare measures. A cloud computing framework based on supervised machine learning algorithms serves as the proposal to predict cardiovascular risks at an early stage. Through a cloud system that stores and analyzes patient data from EHRs and real-time sources the system provides secure and scaleable infrastructure for efficient processing and data analysis. Supervised learning models, including logistic regression and decision trees, together with support vector machines and random forests with gradient boosting, are evaluated in the study for patient risk identification, which depends on clinical data and demographic and lifestyle information. Feature selection methods enhance both interpretability and decrease feature dimensions in models. The measurements used to evaluate models include precision, accuracy as well as recall and F1-score and AUC-ROC. Ensemble prediction models provide superior outcomes to conventional approaches as proven through experimental tests which produce high prediction accuracy. Deployment through the cloud platform enables both remote access to and instant inference capabilities and the integration with medical information platforms. Early cardiovascular risk assessment becomes possible using this method because it supplies both affordability and scalability combined with precision thus enabling medical professionals to deliver timely treatment with customized plans. The proposed predictive model shows immense capability to change preventive cardiology services by using data-driven intelligent healthcare methods in current medical settings.